class_docstr: Conv1d module definition.
  
  Args:
      input_size: Input dimension.
      output_size: Output dimension.
      kernel_size: Size of the convolving kernel.
      stride: Stride of the convolution.
      dilation: Spacing between the kernel points.
      groups: Number of blocked connections from input channels to output channels.
      bias: Whether to add a learnable bias to the output.
      batch_norm: Whether to use batch normalization after convolution.
      relu: Whether to use a ReLU activation after convolution.
      causal: Whether to use causal convolution (set to True if streaming).
      dropout_rate: Dropout rate.
docstr: Create new mask for output sequences.
  
  Args:
      mask: Mask of input sequences. (B, T)
  
  Returns:
      mask: Mask of output sequences. (B, sub(T))
docstr_tok: ["Create","new","mask","for","output","sequences",".","Args",":","mask",":","Mask","of","input","sequences",".","(","B",",","T",")","Returns",":","mask",":","Mask","of","output","sequences",".","(","B",",","sub","(","T",")",")"]
code: def create_new_mask(self, mask: torch.Tensor) -> torch.Tensor:
      """Create new mask for output sequences.
  
      Args:
          mask: Mask of input sequences. (B, T)
  
      Returns:
          mask: Mask of output sequences. (B, sub(T))
  
      """
      if self.padding != 0:
          mask = mask[:, : -self.padding]
  
      return mask[:, :: self.stride]
  
file_path: espnet/espnet2/asr_transducer/encoder/blocks/conv1d.py
imports: from typing import Optional, Tuple, Union
  import torch
label_desc: Use image node_id 5 for calling the Conv1d obj's underlying member method code with example usage: obj.create_new_mask(mask) and returns: mask